短尾负鼠microRNA前体的计算鉴定与分析

目前,关于microRNA(miRNA)的研究越来越多,但是有关负鼠miRNA的研究却相对较少.为了研究灰色短尾负鼠(Monodelphis domestica)的miRNA,以人和小鼠的miRNA为参考序列,分别利用BLAST、比较基因组学两种方法获得可能的短尾负鼠前体序列(pre-miRNA),并各自用机器学习方法分类鉴定,取这两个结果的交集即得到54条新的短尾负鼠pre-miRNA.将这些新发现的pre-miRNA与miRBase19中已经报道的156条pre-miRNA序列合并,设置miRNA基因间距为5 000 bp时,这些miRNA基因可以形成26个基因簇.这些新发现的miRNA均位于编码基因的间区,同时除部分外,新发现的miRNA基因可以归类到31个已知的miRNA家族.最后,计算分析了保守的新发现的39个miRNA基因的靶基因及其功能.     

8种模式生物的miRNA比较分析

目的:分析物种间miRNA序列的共同点和差异点,为后续miRNA研究奠定基础。方法:从miRBase数据库下载8种模式生物,即智人、小鼠、大鼠、果蝇、线虫、拟南芥、水稻、玉米的全部miRNA,通过生物信息学相关软件及方法对其进行分析。结果:各物种成熟miRNA序列长度均约为22 nt,植物miRNA长度分布范围较动物更集中;而pre-miRNA则相反,植物pre-miRNA的长度变异远大于动物;各物种miRNA序列第一个碱基倾向于U,而其他位点的碱基在不同物种间变异较大;miRNA的保守性有一定的范围,不存在在所有物种中均保守的miRNA。结论:找到了miRNA间的一些共同点及差异点,可为后续miRNA鉴定注释提供借鉴。     

基于EST和GSS序列的玉米未知微RNA的数据挖掘

miRNAs通过与靶基因互补位点配对结合,在转录后水平负性调控靶基因的表达.根据miRNA进化上的保守性,以拟南芥、水稻等已知的植物miRNAs为探针,与相关数据库中玉米表达序列标签(EST)和基因组序列(GSS)中的非编码序列比对,采用一系列的标准进行筛选,最后预测得到24个玉米miRNA前体,通过靶基因的预测共得到61个靶基因.通过生物信息学方法大大提高了人们发现miRNAs及其靶基因的效率,补充了玉米miRNA数据库的不足.     

拟南芥基因组中新的microRNA预测及分析

MicroRNA（miRNA）是一类存在于动植物体内、长度为21～25nt的内源性小RNA,对生物体的转录后基因调控起着关键作用,但一些低丰度的miRNA和组织特异性miRNA往往很难发现。为了系统识别拟南芥基因组中新的非同源miRNA,首先基于已报道的拟南芥miRNA的特征,从全基因组范围中筛选出453条可能的miRNA前体;其次,为了进一步对上述miRNA前体进行筛选,利用人的miRNA前体数据构建了支持向量机模型GenomicSVM,该模型对人测试集的敏感性和特异性分别为86.3％和98．1％（30个人miRNA前体和1000个阴性miRNA前体）,对拟南芥测试集的正确率为93．6％（78个miRNA前体）;最后,利用GenomicSVM预测上述453条miRNA前体序列,得到了37条候选的新的拟南芥miRNA前体,为进一步的miRNA实验发现研究提供了指导。     

microRNA多态性与肿瘤发生和药物反应相关

microRNA(miRNA)是进化保守的、非编码单链小RNA,长度约为18-25个核苷酸.作为基因表达的微观管理者,miRNA通过结合在mRNA的3′-UTR抑制翻译过程或使其降解.miRNA多态性是指一类能够干扰miRNA功能的新多态性或单核苷酸的多态性.这种多态性不仅出现在pri-miRNA、pre-miRNA和成熟的miRNA序列中;也可以出现在靶基因3′-UTR;还可以呈现在miRNA基因表观遗传学的改变.miRNA多态性可能导致疾病的产生,也可以判断临床用药疗效及预后监测.目前miRNA多态性正在作为疾病（尤其是肿瘤）生物学研究的强有力工具,而且已应用于疾病的诊断和预后.     

利用TCGA数据库构建肾透明细胞癌相关miRNA预后模型

利用TCGA数据库中肾透明细胞癌的miRNA与mRNA数据及临床信息,构建由miRNA组成的预后风险评分模型,并筛选与生存预后相关的miRNA-mRNA调控关系对,为研究提供理论依据。下载并整理TCGA[JP+1]数据库中肾透明细胞癌的miRNA与mRNA数据;对数据进行差异分析,将差异表达的miRNA与临床信息进行合并,利用单因素与多因素Cox回归分析,构建预后模型并进行模型评价;对模型中的miRNA进行靶基因预测,结果与差异表达的mRNA进行取交集,构建miRNA-mRNA调控网络;对网络中的mRNA进行生存分析,筛选生存相关的miRNA-mRNA调控关系对。共得到49个差异表达的miRNA与3 613个差异表达的mRNA;预后模型计算公式为:风险值(risk score)=hsa-miR-21-5p表达量×0.603＋hsa-miR-1251-5p表达量×-0.093;调控网络中共纳入31个miRNA-mRNA调控关系对;对mRNA进行生存分析,共得到7个有价值的关系对。所构建预后模型可有效预测肾透明细胞癌患者生存预后情况,筛选到的miRNA-mRNA调控关系对可为相关研究与治疗提供参考。     

人源microRNA前体的全基因组预测

microRNA（miRNA）是一类不编码蛋白的调控小分子RNA,在真核生物中发挥着广泛而重要的调控功能.由于miRNA的表达具有时空特异性,因而通过计算方法预测miRNA而后有针对性的实验验证是miRNA发现的一条重要途径.降低假阳性率是miRNA预测方法面临的重要挑战.本研究采用集成学习方法构建预测miRNA前体的分类器SVMbagging,对训练集、测试集和独立测试集的结果表明,本研究的方法性能稳健、假阳性率低,具有很好的泛化能力,尤其是当阈值取0.9时,特异性高达99.90%,敏感性在26%以上,适合于全基因组预测.采用SVMbagging在人全基因组中预测miRNA前体,当取阈值0.9时,得到14933个可能的miRNA前体.通过与高通量小RNA测序数据的比较,发现其中4481个miRNA前体具有完全匹配的小RNA序列,与理论估计的真阳性数值非常接近.最后,对32个可能的miRNA进行实验验证,确定其中2条为真实的miRNA.     

miRNA 的生物合成过程

MicroRNA (miRNA) 是一类真核生物内源性的小分子单链 RNA ,通常为 18 ～ 25 nt 长,能够通过与靶 mRNA 特异性的碱基配对引起靶 mRNA 的降解或者抑制其翻译,从而对基因进行转录后的表达调控 . 近几年来,在动物细胞和植物组织中,上百种 miRNA 被陆续发现 . 这些小分子调控 RNA 是从 60 ～ 200 nt 的具有发夹状结构的前体中被切割出来而成熟的,在动物细胞中, miRNA 基因的转录初产物 (pri-miRMA) 很快被一种核糖核酸酶Ⅲ Drosha 加工成为 miRNA 前体 (pre-miRNA) ,然后由细胞核转运至细胞质中,经另一种核糖核酸酶Ⅲ Dicer 识别剪切为成熟 miRNA. 对这一过程进行了简要的综述,并且对植物 miRNA 的成熟过程也进行了探讨 . 对 miRNA 的生物合成过程的深入了解,将有助于研究这一类起重要调控作用的 RNA 是如何行使功能的,从而进一步研究其在生长发育及各种疾病中所起的重要作用 .     

靶向细胞外基质磷酸糖蛋白的microRNA的筛选及鉴定

目的:寻找靶向细胞外基质磷酸糖蛋白(MEPE)基因的微小RNA(miRNA),并检测其对人HeLa细胞内源性Mepe基因表达的影响。方法:通过NCBI检索人源Mepe的3’UTR,利用miRNA预测工具TargetScan预测可能靶向Mepe的所有miRNA,通过双萤光素酶报告基因系统检测miRNA与Mepe3’UTR的结合情况,从而初步筛选出可能靶向Mepe的miRNA;同时,用Western印迹检测miRNA经转染后对Mepe基因表达的影响。结果:利用TargetScan预测出36条可能靶向Mepe的miRNA,根据分值及匹配情况从中挑选出6条进行验证;与转染空载体pGL3-cm的相对荧光素值相比,转染miR-376a的相对荧光素值降低较为明显,而当Mepe3’UTR与miR-376a结合位点突变后,miR-376a不能抑制萤光素酶的活性;Western印迹结果显示miR-376a能明显抑制MEPE的表达。结论:miRNA-376a可能是靶向Mepe基因的miRNA,为进一步研究MEPE的功能奠定了基础。     

人类基因组miRNA转录调控区保守性DNA序列生物信息学分析

MicroRNA(miPNA)的表达调控方式一直是一个有争议的问题,为了研究miRNA潜在的转录调控特点,本文通过Sanger网站miRNA数据库获得人类miRNA的信息,并建立miRNA相关信息数据库,用MEME和Wordspy两个软件对其上游2 000 bp序列进行保守性分析,得到保守性的DNA序YU(motif),用TESS软件分析保守性DNA序列,预测其转录因子结合情况.通过比较位于基因间、反义链和内含子中的三类不同miRNA转录调控区的保守性和自主转录能力的差异,结果发现位于基因间、反义链上的miRNA上游调控区的保守性比位于内含子的miRNA高,在miRNA的转录调控区存在RNA聚合酶Ⅱ类型的转录因子结合位点,miRNA还表现出自身独特的转录调控方式.通过分析,我们还得到了miRNA表达调控中一些重要的转录因子以及独特的调控序列.本研究结果为miRNA转录调控机制的进一步研究提供了理论依据.     

Global profiling of miRNAs and the hairpin precursors: insights into miRNA processing and novel miRNA discovery

MicroRNAs (miRNAs) constitute an important class of small regulatory RNAs that are derived from distinct hairpin precursors (pre-miRNAs). In contrast to mature miRNAs, which have been characterized in numerous genome-wide studies of different organisms, research on global profiling of pre-miRNAs is limited. Here, using massive parallel sequencing, we have performed global characterization of both mouse mature and precursor miRNAs. In total, 87 369 704 and 252 003 sequencing reads derived from 887 mature and 281 precursor miRNAs were obtained, respectively. Our analysis revealed new aspects of miRNA/pre-miRNA processing and modification, including eight Ago2-cleaved pre-miRNAs, eight new instances of miRNA editing and exclusively 5′ tailed mirtrons. Furthermore, based on the sequences of both mature and precursor miRNAs, we developed a miRNA discovery pipeline, miRGrep, which does not rely on the availability of genome reference sequences. In addition to 239 known mouse pre-miRNAs, miRGrep predicted 41 novel ones with high confidence. Similar as known ones, the mature miRNAs derived from most of these novel loci showed both reduced abundance following Dicer knockdown and the binding with Argonaute2. Evaluation on data sets obtained from Caenorhabditis elegans and Caenorhabditis sp.11 demonstrated that miRGrep could be widely used for miRNA discovery in metazoans, especially in those without genome reference sequences.     

Characteristic comparison between two types of miRNA precursors in metazoan species

MicroRNAs (miRNAs) are a class of small non-coding RNAs discovered in recent years, which are found to play important regulatory roles in various organisms. As the number of experimentally validated miRNAs is rapidly increasing, systematic analysis on the characteristics of these known miRNAs is necessary and indispensable, especially for computational prediction of new miRNAs. We extensively analyzed precursor sequences for all experimentally validated mature miRNAs in metazoan species, focusing on the characteristics at the level of primary sequences and secondary structures. An observation over the secondary structures of 2729 miRNA precursors (pre-miRNAs) reveals that these hairpin structures can be approximately classified into two types: one with a hairpin loop, and the other with multiple loops. Interestingly, the two types of pre-miRNAs show significant differences in both sequence and structure characteristics, and our study indicates that separate consideration on each type of pre-miRNAs is more reasonable, especially in computational prediction. Besides, we develop a new criterion called mAMFE which shows robust discriminative power in distinguishing pre-miRNAs against other RNAs, thus can potentially serve as a discriminative feature in prediction of new pre-miRNAs.     

Discovery of novel microRNAs in rat kidney using next generation sequencing and microarray validation

MicroRNAs (miRNAs) are small non-coding RNAs that regulate a variety of biological processes. The latest version of the miRBase database (Release 18) includes 1,157 mouse and 680 rat mature miRNAs. Only one new rat mature miRNA was added to the rat miRNA database from version 16 to version 18 of miRBase, suggesting that many rat miRNAs remain to be discovered. Given the importance of rat as a model organism, discovery of the completed set of rat miRNAs is necessary for understanding rat miRNA regulation. In this study, next generation sequencing (NGS), microarray analysis and bioinformatics technologies were applied to discover novel miRNAs in rat kidneys. MiRanalyzer was utilized to analyze the sequences of the small RNAs generated from NGS analysis of rat kidney samples. Hundreds of novel miRNA candidates were examined according to the mappings of their reads to the rat genome, presence of sequences that can form a miRNA hairpin structure around the mapped locations, Dicer cleavage patterns, and the levels of their expression determined by both NGS and microarray analyses. Nine novel rat hairpin precursor miRNAs (pre-miRNA) were discovered with high confidence. Five of the novel pre-miRNAs are also reported in other species while four of them are rat specific. In summary, 9 novel pre-miRNAs (14 novel mature miRNAs) were identified via combination of NGS, microarray and bioinformatics high-throughput technologies.     

MiRANN: a reliable approach for improved classification of precursor microRNA using Artificial Neural Network model

MicroRNA (miRNA) is a special class of short noncoding RNA that serves pivotal function of regulating gene expression. The computational prediction of new miRNA candidates involves various methods such as learning methods and methods using expression data. This article has proposed a reliable model - miRANN which is a supervised machine learning approach. MiRANN used known pre-miRNAs as positive set and a novel negative set from human CDS regions. The number of known miRNAs is now huge and diversified that could cover almost all characteristics of unknown miRNAs which increases the quality of the result (99.9% accuracy, 99.8% sensitivity, 100% specificity) and provides a more reliable prediction. MiRANN performs better than other state-of-the-art approaches and declares to be the most potential tool to predict novel miRNAs. We have also tested our result using a previous negative set. MiRANN, opens new ground using ANN for predicting pre-miRNAs with a promise of better performance.     

Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

Background  

MicroRNAs (miRNAs) are a group of short (~22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology.